Responsible AI
40-45%of exam
Models + Workloads
40-45%of exam
Foundry Apps
55-60%of exam
Foundry Tools
55-60%of exam
Quick Facts
- Exam
- AI-901
- Credential
- Azure AI Fundamentals
- Status
- Beta
- Time
- 45 minutes
- Pass
- 700/1000
- Questions
- 40-60 MCQ
- Fee
- $99 US
- Blueprint
- Apr 15 2026
- Largest
- Foundry 55-60%
- Code
- Python basics
Responsible AI
F R P I T A
FairnessReliability+safetyPrivacy+securityInclusivenessTransparencyAccountability
Responsible AI
- Fairness
- Equitable outcomes
- Reliability
- Consistent and safe
- Privacy
- Protect user data
- Security
- Protect connected systems
- Inclusiveness
- Accessible for everyone
- Transparency
- Explain limits
- Accountability
- People remain responsible
- Review
- Humans approve risk
Safety Controls
- Content filters
- Block harms
- Hate
- Identity attacks
- Sexual
- Adult content
- Violence
- Physical harm
- Self-harm
- Self injury
- Prompt Shields
- Prompt attack detection
- Protected material
- Known text/code
- Groundedness
- Checks source support
Token Limits
Tokens bound cost, context, length
Input tokensOutput tokensContext capMax response
RAG vs Fine-tune
RAG
- Retrieves sources
- Fresh/private data
- No weight change
Fine-tune
- Learns examples
- Changes weights
- Stable task style
Ground vs adapt
Model Picker
- Need chat text→Chat model(Conversation)
- Need semantic search→Embeddings(Vectors)
- Need image input→Multimodal(Vision prompt)
- Need image output→Image generation(New image)
- Need private facts→RAG(Grounding)
- Need repeated style→Fine-tune(Examples)
- Need lower cost→Smaller model(Latency)
- Need residency→Region choice(Compliance)
Exam Snapshot
- AI-901
- Azure AI Fundamentals
- Status
- Beta exam
- Updated
- Apr 15 2026
- Pass
- 700/1000
- Time
- 45 minutes
- Format
- Multiple choice
- Fee
- $99 US
- Provider
- Pearson VUE
- Practice test
- Not yet available
Classification vs Regression
Classification
- Predicts category
- Spam/not spam
- Discrete output
Regression
- Predicts number
- Demand forecast
- Continuous output
Label vs number
Model Basics
- Token
- Text unit
- Context window
- Input/output limit
- Embedding
- Meaning vector
- Temperature
- Randomness control
- Max tokens
- Output cap
- System message
- High-priority rules
- User message
- Task request
- Deployment
- Callable model
- Endpoint
- Request URL
AI Workloads
- Generative AI
- Creates content
- Predictive AI
- Estimates outcomes
- Agentic AI
- Acts with tools
- Text analysis
- Understands text
- Speech
- Processes audio
- Vision
- Processes images
- Extraction
- Structures content
- Translation
- Changes language
ML Tasks
- Classification
- Predicts category
- Regression
- Predicts number
- Clustering
- Groups unlabeled data
- Forecasting
- Predicts future values
- Anomaly detection
- Finds outliers
- Training
- Learns from data
- Inference
- Uses trained model
Foundry Flow
Catalog | Deploy | Test | Code
CatalogDeploymentPlaygroundSDK/app
Catalog vs Deployment
Catalog
- Compare options
- Model cards
- Capabilities/cost
Deployment
- Creates endpoint
- Named model
- App callable
Choose vs use
Build Picker
- Compare models→Catalog
- Call model→Deployment
- Try prompt→Playground
- Create app→SDK
- Connect data→Connection
- Use actions→Agent
- Inspect behavior→Tracing
- Share stable agent→Publish
Foundry Flow
- Foundry resource
- Azure AI container
- Project
- Solution workspace
- Catalog
- Compare models
- Deployment
- Creates endpoint
- Playground
- Test prompts
- View code
- Copy client call
- SDK
- Build app
- Connection
- Secures data link
- RBAC
- Controls access
Playground vs SDK
Playground
- Portal test
- Tune prompts
- No code
SDK
- Application code
- Project client
- Production path
Prototype vs build
Prompting
- System prompt
- Sets behavior
- User prompt
- Asks task
- Few-shot
- Shows examples
- Zero-shot
- Instruction only
- Constraints
- Format boundaries
- Grounding
- Adds sources
- Temperature
- Varies output
- Max tokens
- Limits length
Chat vs Agent
Chat
- Single response
- No tool plan
- Prompt driven
Agent
- Multi-step goal
- Uses tools
- Managed runtime
Answer vs act
Agents
- Agent
- Model plus tools
- Instructions
- Goal and rules
- Tools
- External actions
- Runtime
- Runs agent
- Responses API
- Agent entry point
- Memory
- Conversation state
- File search
- Finds project files
- Publish
- Stable endpoint
- Tracing
- Observe steps
System vs User Prompt
System
- Role rules
- Higher priority
- Behavior guard
User
- Task request
- Conversation input
- Lower priority
Rules vs request
Workload Inputs
Text, speech, image, document
Language: textSpeech: audioVision: imageCU: multimodal
OCR vs Extraction
OCR
- Reads text
- Lines/words
- Image/document input
Extraction
- Returns fields
- Schema output
- Confidence scores
Text vs fields
Workload Picker
- Analyze text→Language
- Find text entities→NER
- Transcribe audio→Speech to text
- Speak response→Text to speech
- Translate speech→Speech translation
- Read image text→OCR
- Generate image→Image model
- Extract form fields→Content Understanding
- Process video→Content Understanding
Text + Speech
- Language
- Text analytics
- Sentiment
- Opinion polarity
- Key phrases
- Main terms
- NER
- Typed entities
- Summarization
- Shorter meaning
- Speech to text
- Audio transcript
- Text to speech
- Spoken output
- Speech translation
- Translated audio/text
- Speaker recognition
- Identifies voice
Speech vs Speaker
Speech recognition
- What was said
- Transcript
- Captions
Speaker recognition
- Who spoke
- Voice traits
- Verify person
Words vs person
Vision + Images
- Vision
- Image analysis
- OCR
- Reads visible text
- Object detection
- Labels locations
- Image captions
- Describes scene
- Multimodal
- Text plus image
- Visual input
- Prompt image reasoning
- Image generation
- Creates new image
- Image safety
- Flags harmful images
Vision vs Generation
Vision
- Analyzes image
- OCR/objects
- Existing content
Generation
- Creates image
- Prompt driven
- New content
Inspect vs create
Content Understanding
- Documents
- Forms and files
- Images
- Visual fields
- Audio
- Speech insights
- Video
- Scenes and moments
- Analyzer
- Extraction configuration
- Schema
- Desired fields
- Confidence
- Reliability score
- Grounding
- Source region
- Markdown
- Search output
- JSON
- Automation output
Common Traps
Facts vs fluency
Model sounds confident ≠ Sources prove answers
Catalog vs deployment
Catalog lists models ≠ Deployment makes callable
Prompt vs fine-tune
Prompt changes context ≠ Fine-tune changes weights
Agent vs chatbot
Chat replies once ≠ Agent uses tools
OCR vs extraction
OCR reads text ≠ Extraction returns fields
Speech vs speaker
Speech recognizes words ≠ Speaker recognizes person
Vision vs generation
Vision analyzes images ≠ Generation creates images
Fairness vs accuracy
Fairness checks groups ≠ Accuracy averages performance
Last Minute
- 1.Blueprint: 40-45 / 55-60
- 2.Pass: 700/1000
- 3.Beta updated Apr 15 2026
- 4.Foundry domain is larger
- 5.FRPITA = responsible AI
- 6.Catalog then deploy
- 7.Playground before SDK
- 8.RAG grounds; fine-tune adapts
- 9.System prompt sets rules
- 10.Tokens drive cost and limits
- 11.Agent = model + tools
- 12.Language = written text
- 13.Speech = audio
- 14.Vision = images
- 15.Content Understanding extracts fields
- 16.OCR reads visible text
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